Efficient equilibrium sampling of all-atom peptides using library-based Monte Carlo

Ying Ding, Artem B. Mamonov, Daniel M. Zuckerman

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We applied our previously developed library-based Monte Carlo (LBMC) to equilibrium sampling of several implicitly solvated all-atom peptides. LBMC can perform equilibrium sampling of molecules using precalculated statistical libraries of molecular-fragment configurations and energies. For this study, we employed residue-based fragments distributed according to the Boltzmann factor of the optimized potential for liquid simulations all-atom (OPLS-AA) forcefield describing the individual fragments. Two solvent models were employed: a simple uniform dielectric and the generalized Born/surface area (GBSA) model. The efficiency of LBMC was compared to standard Langevin dynamics (LD) using three different statistical tools. The statistical analyses indicate that LBMC is more than 100 times faster than LD not only for the simple solvent model but also for GBSA.

Original languageEnglish (US)
Pages (from-to)5870-5877
Number of pages8
JournalJournal of Physical Chemistry B
Issue number17
StatePublished - May 6 2010
Externally publishedYes

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Surfaces, Coatings and Films
  • Materials Chemistry


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